Statistical Analysis Techniques Research Articles

Factors Influencing the Utilization of Health Services for JKN-KIS Participants (Study: Grilled Pematang III.B Health Center, Mesuji Makmur District, Ogan Komering Ilir Regency)

This study aims to determine whether there is an influence of the level of knowledge of JKN-KIS participants, the attitude of health workers and the accessibility of health facilities simultaneously on the utilization of health services for JKN-KIS participants at Community Health Centers and their networks.The method used in this study is a quantitative method. Minimum of 99 samples from JKN-KIS participants in the work area of ​​Pematang Panggang III.b Health Center, Mesuji Makmur District, Ogan Komering Ilir Regency. In this study, there were two hypothesis tests, namely the Partial Significance Test (T-Test) and the Simultaneous Test (F-Test). The data analysis technique used descriptive statistical analysis techniques; multiple linear regression analysis. The results showed that the level of knowledge of JKN-KIS participants did not have a significant effect on the utilization of health services at the Pematang Panggang III.b Health Center, Mesuji Makmur District and its network. The accessibility of health facilities had a positive and significant effect on the utilization of health services for JKN-KIS participants at the Pematang Panggang III.b Health Center, Mesuji Makmur District. The level of knowledge of JKN-KIS participants, the attitude of health workers and the accessibility of health facilities simultaneously had a positive and significant effect on the utilization of health services for JKN-KIS participants at the Pematang Panggang III.b Health Center, Mesuji Makmur District, Prosperous Mesuji.

Automated methodology for calculating key production indicators of oil field development for business planning model

Background: In order to secure the stable and efficient operations of oil-producing companies, it is essential to forecast the profitability and ROI of the enterprise, as well as to regulate development for the next 5 years. Accurate forecasts facilitate more informed planning and decision-making, directly influencing the economic sustainability and competitiveness of the enterprise. Aim: The purpose of this study is to develop an innovative approach to automate the methodology for calculating key development indicators in a business planning model. Materials and methods: The study utilizes methods for collecting and analyzing production and geological data, empirical forecasting models, and statistical analysis techniques to enchance the accuracy and reliability of forecasts. This approach employs modern algorithms and technologies to process large volumes of data, which allows for more accurate and reasonable forecasts of key production indicators of the field development. Results: This methodology can be applied to forecast a five-year business plan and evaluate its expected implementation. It is integrated into the ‘Production planning and monitoring’ of the ABAI information system, which allows direct data export from the database, automates the monthly monitoring of production indicators, and generate reports for further export. Conclusion: The proposed method for automated planning of key production indicators of the development enhances the accuracy and efficiency of forecasting, thereby improving the quality of planning and evaluating the implementation of the business plan. This contributes to more informed and strategically validated management of oil production processes. Automation of planning processes reduces the labor costs traditionally associated with manual analysis and calculations, freeing up resources for more strategic purposes. This enables rapid responses to changes in production conditions and prompt adjustments of plans. As a result, managers can allocate resources more efficiently, minimize risks, and increase the overall productivity of oil production operations.

Exploring the correlation between DNA methylation and biological age using an interpretable machine learning framework

DNA methylation plays a significant role in regulating transcription and exhibits a systematic change with age. These changes can be used to predict an individual’s age. First, to identify methylation sites associated with biological age; second, to construct a biological age prediction model and preliminarily explore the biological significance of methylation-associated genes using machine learning. A biological age prediction model was constructed using human methylation data through data preprocessing, feature selection procedures, statistical analysis, and machine learning techniques. Subsequently, 15 methylation data sets were subjected to in-depth analysis using SHAP, GO enrichment, and KEGG analysis. XGBoost, LightGBM, and CatBoost identified 15 groups of methylation sites associated with biological age. The cg23995914 locus was identified as the most significant contributor to predicting biological age by calculating SHAP values. Furthermore, GO enrichment and KEGG analyses were employed to initially explore the methylated loci’s biological significance.

Determination of Factors Affecting Farmers' Preference for Corn Seed by Conjoint Analysis

Modeling consumer preferences among multi-attribute alternatives has been one of the main activities in consumer research. Conjoint analysis is a multivariate statistical analysis technique that reveals the acceptance of criteria such as yield, price and adaptability that are important in consumers' usage habits and preferences. In this study, it was aimed to determine the degree of importance given by grain corn growers to seed traits in corn seed purchase. For this purpose, conjoint analysis was applied and preferences in seed purchase were determined according to the data obtained from the survey conducted among farmers. The survey questions used in the research were applied to 123 farmers. Kendall's Tau value, which is the representativeness of the model, was 0.935 (p

Automated Verification of Parametric Channel-Based Process Communication

A challenge of writing concurrent message passing programs is ensuring the absence of partial deadlocks, which can cause severe memory leaks in long running systems. Several static analysis techniques have been proposed for automatically detecting partial deadlocks in Go programs. For a large enterprise code base, we found these tools too imprecise to reason about process communication that is parametric, i.e., where the number of channel communication operations or the channel capacities are determined at runtime. We present a novel approach to automatically verify the absence of partial deadlocks in Go program fragments with such parametric process communication. The key idea is to translate Go fragments to a core language that is sufficiently expressive to represent real-world parametric communication patterns and can be encoded into Dafny programs annotated with postconditions enforcing partial deadlock freedom. In situations where a fragment is partial deadlock free only when the concurrency parameters satisfy certain conditions, a suitable precondition can often be inferred. Experimental results on a real-world code base containing 583 program fragments that are beyond the reach of existing techniques have shown that the approach can verify the absence of partial deadlocks in 145 cases. For an additional 228 cases, a nontrivial precondition is inferred that the surrounding code must satisfy to ensure partial deadlock freedom.

Optimized classification of diabetes using dynamic waterwheel plant optimization algorithm.

The classification of chronic diseases has been a prominent research focus in public health, extensively leveraging machine learning algorithms. One of these chronic diseases that has significant rates of occurrence all around the world is diabetes, which is a disease by itself. Many academics are working to construct robust machine-learning algorithms for accurate categorization, given the prevalence of this chronic disease. A revolutionary methodology that can accurately categorize diabetic disease is the focus of this study, which aims to provide new methods. The proposed technique in this work is based on developing a novel feature selection method, DWWPA, which stands for dynamic waterwheel plant algorithm. The DWWPA algorithm is utilized in the process of optimizing the K-nearest neighbors (KNN) model in order to improve the accuracy of its classification. In the feature selection process, a binary representation of this method is called binary DWWPA (bDWWPA). Several different machine learning models and optimization techniques are compared to the strategy that has been presented. When categorizing diabetes cases in the dataset, the findings demonstrate the superiority and success of the proposed method. Furthermore, several different statistical analysis techniques, such as Analyses of variance (ANOVA) and Wilcoxon signed-rank test, are carried out to investigate the statistical difference and importance of the suggested strategy in contrast to the other ways at the same level of competition. The conclusions of these tests were consistent with what was anticipated they would be. Based on the suggested feature selection and the optimization of the KNN model, the proposed method has an accuracy of 98.9% when taken as an entire. The suggested method was useful in accurately classifying diabetic disease, as evidenced by the fact that it achieved a higher level of accuracy than the contemporary approaches.

An Empirical Study on Indian Economy Over COVID-19 Pandemic

The infectious disease coronavirus disease 2019 (COVID-19) emerged as a human pathogen and contributed to a pandemic around the world. According to the reports released as of February 2021, around 103 million confirmed cases and 2,247,917 deaths are registered. The pandemic of COVID-19, or coronavirus, has exposed several global system vulnerabilities. Even after having all the abilities to handle hazards, the sudden outbreak of COVID-19 isolated every one of us from others. COVID-19 has caused the Indian economy to experience considerable disruption. The current pandemic of the coronavirus may lead to a permanent loss of 4 per cent of real Indian gross domestic product (GDP). Some key sectors such as tourism and aviation, telecommunication, automobiles and transport are negatively impacted. The other sectors such as e-learning, e-retail, logistics, pharmaceuticals and so on benefit from COVID-19. The present research is intended to study the positive and negative effects of COVID-19 in different industries, taking into account data of a secondary type. The survey also concludes with the initiatives, schemes and market effects for five important sectors using various relevant statistical instruments and techniques for analysis and conclusion.

DETERMINANTS OF ZAKAT PAYMENT: THE VIEWPOINT OF STAKEHOLDERS IN SEREMBAN

Islamic countries widely recognize zakat as a significant means to promote equity in sharing wealth among Muslims. Despite the obligation of Muslims to pay zakat, the total amount collected is considerably lower than the amount acquired through taxes. Thus, this study aims to identify the determinants influencing zakat payment among Muslims in Seremban, Negeri Sembilan. It will provide a better understanding of the factors that shape zakat payment behavior in this district. The data collection method involved a quantitative survey using structured questionnaires administered to a sample of 400 Muslim residents in Seremban. The study utilized a range of statistical analysis techniques, including correlation and regression to examine the relationships between the determinants and zakat payment practice. The study found that convenience is the most significant determinant motivating individuals to pay zakat through zakat agencies. This finding provides valuable insights for zakat agencies, policymakers, and religious authorities in Malaysia to develop targeted strategies to increase zakat compliance behavior among Muslims. These may include improving religious education programs, implementing transparent reporting mechanisms, developing tailored outreach programs, leveraging technology to simplify and streamline the zakat payment process, and collaborating with community leaders to reinforce positive social norms in paying zakat.

Assessing drinking water quality in Eloued, south-East Algeria, using the groundwater pollution index (GPI) and the synthetic pollution index (SPI) model.

Groundwater quality degradation is a significant environmental issue worldwide, with potentially severe economic consequences and harm to ecosystems and biodiversity. This can directly affect human health, particularly in developing countries where rapid and uncontrolled urbanization is on the rise. Groundwater is the primary resource for meeting the water needs of the Eloued region, located in southeastern Algeria. Water is considered unfit for human consumption if its physico-chemical elements exceed national or international standards or guidelines. We used the GPI and SPI indices to evaluate the quality of groundwater suited for drinking. Groundwater samples were obtained from 22 wells at depths of more than 250m. Standard analytical procedures were used to determine the physicochemical characteristics of the collected samples, which included pH, EC, TDS, Na+, Ca+2, Mg+2, K+, Cl-, HCO3-, SO4-2, NO3-, NO2-, NH4+ and PO4-3. Multivariate statistical analysis and GIS techniques were used to process the results. The results of the selected physicochemical parameters were compared with World Health Organization (WHO) guidelines to determine the quality of drinking water. The findings indicate that the waters of the terminal complex aquifer are salty and contain medium to high quantities of main ions that surpass the established drinking water limits. The primary ions' relative abundance is Cl- > SO4-2 > HCO3- > NO3 for anions and Na+ > Ca+2 > Mg+2 > K+ for cations. Groundwater chemical types were dominated by Na+, Ca+2, Cl-, and SO4-2. Principal Component Analysis (PCA) showed that alteration and dissolution of carbonates, evaporates, salts, partly silicates, and evaporation, are the main reasons affecting the chemical composition of water in Eloued. The GPI results show that 18.18%, 54.54%, and 27.27% of the water samples were classed as lightly polluted, moderately polluted, or substantially polluted for drinking purposes, respectively. According to the SPI study, 9.09%, 36.36%, 36.36%, and 18.18% were considered drinkable, mildly contaminated, moderately polluted, and seriously polluted for drinking purposes, respectively. According to the GPI and SPI models' geographical distribution maps, potable water is generally scarce and concentrated in the northeastern section of the research area, near the town of Ourmes.

Symbolic testing of floating-point bugs and exceptions

Numerical software are susceptible to floating-point bugs and exceptions, which may lead to severe threats like denial of service attacks. Static analysis techniques such as symbolic execution are effective in detecting general bugs which often cause memory error or program crash. Unfortunately, these methods do not deal well with numerical code as they do not support floating-point constraints and math functions symbolically. In this paper, we propose a new analysis framework YUSE, which can detect floating-point bugs by constructing constraints and exploring paths which contain floating-point expressions. Specifically, we introduce interval computation and interval constraint propagation in non-relational numerical abstract domains, and symbolically model math functions, to accurately detect floating-point bugs and exceptions. Moreover, we leverage two-phase constraint solving to enhance YUSE’s performance. Experimental results show that YUSE outperforms two state-of-the-art tools, Frama-c and Fpse-study, in terms of effectiveness and efficiency, with 1.4× and 7.1× faster than Frama-c and Fpse-study, respectively. Moreover, YUSE found 20 new bugs in real-world software, 12 of which were assigned CVE IDs and 8 of which were confirmed by developers.

Rythu Bharosa Kendram: Rejuvenating the Indian Rural Economy

Rythu Bharosa Kendram (RBK) serves as one stop shop for all of the needs of farmers at the Panchayat level. They offer services like e-crop booking for crop insurance, an agricultural input store, farmer knowledge centers, a technical advisory team, YSR Rythu Bharosa, Polambadi (Farmer Field School), CCRC cards, D-Krishi Seed distribution, and others. The objective is to study the extent of adoption of technologies and services of RBKs by the farmers. The present study is conducted in Prakasam district of Andhra Pradesh state as it is has significant area under crop cultivation. Multi stage simple random sampling technique has been adopted to select the sample farmers for the study. The data was collected from 75 respondents by personal interview method and analyzed using statistical analytical techniques such as Mean scale value and adoption Index. The study revealed that the majority of respondents 58.67% adopted technology at a medium level followed by low adoption 26.67% and high adoption 14.67% levels. The study concludes that RBKs are vital in serving farmers, with moderate adoption levels influenced by farmers’ age and education.

Automatic Refactoring Approach for Asynchronous Mechanisms with CompletableFuture

To address the inherent limitations of Future in asynchronous programming frameworks, JDK 1.8 introduced the CompletableFuture class, which features approximately 50 different methods for composing and executing asynchronous computations and handling exceptions. This paper proposes an automatic refactoring method that integrates multiple static analysis techniques, including visitor pattern analysis, alias analysis, and executor inheritance structure analysis, to conduct precondition checks. Distinct from existing Future refactoring methods, this approach considers custom executor types, thereby extending its applicability. Using this method, the ReFuture automatic refactoring plugin was implemented within the Eclipse JDT framework. The method was evaluated in terms of the number of refactorings, refactoring time, and error introduction, alongside a side-by-side comparison with the existing method. The refactoring outcomes for nine large applications, including ActiveMQ, Hadoop, and Elasticsearch, show that ReFuture successfully refactored 639 out of 813 potential code structures, achieving a refactoring success rate of 64.70% without introducing errors. This tool effectively facilitates the refactoring to CompletableFuture and enhances refactoring efficiency compared to manual methods.

Pengaruh Pergaulan terhadap Motivasi Belajar Mahasiswa PAI Angkatan 2020 Universitas Neegri Padang

Getting along should be a relationship between fellow human beings. Human to another human being is established through a time-cycle process that is relatip long time then influenced and influenced in increasing learning motivation, it is necessary driving force that stimulates interest in learning and allows for continuous learning. Continuous learning. Through learning motivation can be interpreted as a reciprocal force through one's internal and external self when carrying out the learning process. When carrying out the learning process. The study aims to determine the effect of socialization on the learning motivation of UNP 2020 PAI students. Quantitative research through the type of correlation and in the organization through statistical analysis techniques descriptive statistical analysis technique. Data collection presents primary data as well as validity and reliability tests. Through determining the sample of 2020 PAI students as many as 60 people. The whole data is allocated based on instrument questionnaires from the socialization and learning motivation variables. Overall results of the data analysis were calculated and tested through statistical calculations and correlation of the mean found using SPSS software. The data results attach a positive significant vibes of association to learning motivation 59.3%. The results show that overall socialization has a significant influence on learning motivation, meaning that learning motivation is influenced by socialization. is influenced by socialization. Researchers recommend that be smart in choose socialization.

INTEGRATING LAND USE ANALYSIS WITH WATER DEMAND ESTIMATION: A CASE STUDY OF PUTRAJAYA, MALAYSIA

The challenges posed by population growth, urbanization, and changing land use patterns on sustainable water resource management are significant. This paper puts forth an integrated framework aimed at assessing future water demand in Putrajaya, Malaysia. The proposed framework combines population projections, estimations of water demand, and analyses of land use activities. Through an examination of demographic trends and land use patterns, the framework predicts population growth and identifies areas with high water demand. Daily water use patterns in homes and businesses (temporal analysis) inform the designing future water infrastructure, incorporating temporal aspects. Statistical and spatial analysis techniques are then utilized to merge these projections with water demand estimations to quantify water requirements in various zones and types of land use. This study has unveiled two daily peaks in water demand, which align with household schedules. Residential areas emerge as the primary consumers of water, displaying an evening peak distinct from the midday peak seen in businesses. The current water demand in Putrajaya is estimated at 94 million litres per day, with domestic usage surpassing non-domestic usage in a ratio of 3:2. Projections based on future land use plans foresee a 19% increase in demand, underscoring the urgency for proactive water management strategies. Spatial analysis has highlighted residential areas as the main users of water, with demand levels varying throughout the city. By comprehending these temporal and spatial patterns, water authorities can strategically target interventions, optimize infrastructure siting, and forecast future demand trends. These proactive measures are essential for securing a sustainable water future for Putrajaya.

Functional Data Analysis: An Introduction and Recent Developments.

Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as descriptive analyses, classification, and regression, are generally the same as for statistical analyses of scalar-valued or multivariate data, but FDA brings additional challenges due to the high- and infinite dimensionality of observations and parameters, respectively. This paper provides an introduction to FDA, including a description of the most common statistical analysis techniques, their respective software implementations, and some recent developments in the field. The paper covers fundamental concepts such as descriptives and outliers, smoothing, amplitude and phase variation, and functional principal component analysis. It also discusses functional regression, statistical inference with functional data, functional classification and clustering, and machine learning approaches for functional data analysis. The methods discussed in this paper are widely applicable in fields such as medicine, biophysics, neuroscience, and chemistry and are increasingly relevant due to the widespread use of technologies that allow for the collection of functional data. Sparse functional data methods are also relevant for longitudinal data analysis. All presented methods are demonstrated using available software in R by analyzing a dataset on human motion and motor control. To facilitate the understanding of the methods, their implementation, and hands-on application, the code for these practical examples is made available through a code and data supplement and on GitHub.

THE INFLUENCE OF THE STRUCTURAL ANALYTICAL METHOD (SAS) WITH THE ASSISTANCE OF BIG BOOK MEDIA ON ELEMENTARY SCHOOL STUDENTS' BEGINNING READING ABILITY

This research was motivated by the results of UNESCO and PISA which stated that the literacy rate in Indonesia is still low, especially in beginning reading in elementary school students. The purpose of this study was to measure the initial reading ability of elementary school students, especially in grade I students. This research is a quasi-experimental research with a quantitative approach. Sampling was carried out using purposive sampling techniques. In obtaining data, researchers conducted initial tests (pretest) and final tests (posttest) in the form of preliminary reading tests, observation sheets and interviews. The data technique used is to use descriptive analysis techniques and descriptive statistical analysis by testing hypotheses using t-test statistics.. The data obtained are then analyzed using statistical analysis techniques, namely descriptive statistics. Descriptive statistical analysis data obtained average value (pretest) = 69,17 is in the very low category and average value (posttest) = 83,00 is in the high category. From the results of the hypothesis test analysis using paired sample test, a sig value = 0,001 is obtained in accordance with the basis for decision making, if the hypothesis test of the probability value of significance < 0.05, then H0 is rejected and Ha is accepted. Then significantly the alternative hypothesis (Ha) is accepted and the null hypothesis (H0) is rejected. Based on the hypothesis test, it can be concluded that the synthetic analytical structural method (SAS) with the help of big book media affects the initial reading ability in grade I students of SDN Kebalen 01 Kabupaten Bekasi.

Hydro-Climatic Factors and Health Problems in the Town of Ouéllé (Central-Eastern Côte d'Ivoire)

This article deals with hydro-climatic factors and their health impacts in the town of Ouéllé in a context of climate change. The aim of this study is to show the impact of hydro-climatic determinants on the spatial distribution of pathologies in the town of Ouéllé. The methodological approach used was based on data collection techniques ranging from documentary research to field surveys. Interviews were conducted with experts, while questionnaires were administered to 100 people living in the urban area of Ouéllé. In addition, photographs of the spatial units were taken using a Redmi 9A smartphone, followed by the collection of geospatial data using the UTM Geo map mobile application for data collected in KML (Keyhole Markup Language) format on the Android operating system. We also used statistical data analysis techniques to process the data. The results showed that the main hydro-climatic factors in Ouéllé are rainfall, temperature and wind. The diseases linked to these hydro-climatic factors are malaria, which is the main health risk, accounting for 63% of consultations in 2023, followed by typhoid fever (8%) and acute respiratory infections (ARI) (3%).

Financial Factors and Beyond: A Survey of Credit Risk Assessment for VSBs by Moroccan Banks

This article analyzes the extent to which Moroccan banks adopt a holistic approach to credit risk assessment, incorporating criteria that go beyond simple balance sheets and financial ratios. To this end, Multiple Correspondence Analysis and advanced statistical techniques were employed using R software. A detailed questionnaire comprising 14 questions, was distributed to a sample of Moroccan bankers. The results of this analysis reliably confirm our initial hypotheses. They reveal a growing trend among Moroccan banks towards a more complex and nuanced assessment of credit risk. In addition to traditional financial aspects such as financial statement analysis, banks are increasingly considering non-financial criteria such as collateral requirements, quality of corporate governance, sustainability of business practices, and technological positioning. This diversified approach gives banks a more accurate and comprehensive view of a company's risk profile. It also enables them to make better-informed and more balanced lending decisions, considering the multitude of factors that can influence a company's ability to repay.

Pengaruh Media Fun Learning Card Terhadap Hasil Belajar Peserta Didik Kelas XI SMAN 21 Surabaya

Education plays an important role in shaping learners' competencies, including cognitive, affective and psychomotor aspects to face the challenges of the times. In Indonesia, education should look at holistic aspects that include character development, life skills and social abilities as mandated in Law No. 20/2003. However, challenges in classroom management and learning boredom often become obstacles. For this reason, innovative approaches are needed, such as the use of interactive learning media. This study aims to examine the effect of using Fun Learning Card media on the learning outcomes of grade XI students at SMA Negeri 21 Surabaya. This media is expected to significantly increase student participation and understanding, overcome boredom, and create more meaningful learning. By using descriptive statistical analysis techniques, standard deviation, and learning outcome categories, the following results were found before using the learning media in cycle 1 with an average score of 49.6. The score was obtained from the lowest score of 35 and the highest score of 70, then also obtained a standard deviation score (P) of 10.2. In cycle 2 using Fun Learning Card media, the highest score was 97 while the lowest score was 76 with an average score of 85.4 and a standard deviation of 6.3.

Predicting time to cracking of concrete composites under restrained shrinkage: A review with insights from statistical analysis and ensemble machine learning approaches

Restrained shrinkage cracking significantly undermines the performance of reinforced concrete and shortens the structures' lifespan. Standard methods have been established to examine restrained shrinkage cracking of cementitious composites, i.e., ASTM C1581, AASHTO T 334, AASHTO T 363, and RILEM TC 119-TCE. Deficiencies in testing methods for accommodating and evaluating different cementitious composites have been recognized. Nevertheless, the significance of the restrained shrinkage cracking factors and cracking vulnerability based on the degree of restraint provided by different testing methods remain unexplored. This article provides a critical review of testing methods and studies on restrained shrinkage cracking in cementitious composites, highlighting the limitations, advantages, and further capabilities. Comprehensive experimental test datasets associated with standard restrained shrinkage cracking test methods are compiled. Advanced statistical analysis and machine learning (ML) techniques are utilized to predict time-to-cracking (TTC) of cementitious composites subjected to various testing methods based on the cracking factors. Robust and precise ML's bagging and boosting algorithms such as random forest, gradient boosting machine, extreme gradient boosting, adaptive boosting, categorical gradient boosting, and stacking ensemble models are utilized to merge predictions of the various approaches on TTC. Despite the satisfactory performance of statistical modeling, stacking demonstrates the highest precision in forecasting TTC, achieving an R2 of 0.92 for the ASTM dataset and 0.65 for the AASHTO dataset. The derived statistical and ML models reveal the significance of drying shrinkage, binder content, and moist curing as the most important factors in TTC based on ASTM C1581 and AASHTO T 334 standard testing methods. Equations are derived to predict cracking vulnerability and design concrete composites that offer delayed TTC and prolonged service life.